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A kinship based method of measuring genetic diversity

A kinship based method of measuring genetic diversity. Herwin Eding ID-Lelystad Lelystad, The Netherlands. Short outline of presentation. Definition of genetic diversity Why kinships? Marker Estimated Kinships Similarity index Accounting for probability AIS Core set diversity

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A kinship based method of measuring genetic diversity

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  1. A kinship based method of measuring genetic diversity Herwin Eding ID-Lelystad Lelystad, The Netherlands

  2. Short outline of presentation • Definition of genetic diversity • Why kinships? • Marker Estimated Kinships • Similarity index • Accounting for probability AIS • Core set diversity • Application

  3. Definition Genetic Diversity • Maximum genetic variation of a population in HW-equilibrium derived from a set of conserved breeds

  4. Kinships and genetic diversity (1) • VGw = VG[1 – fw] • (Falconer and MacKay, 1996) • Diversity proportional to (1- fW) • Max(diversity) => min(fW)

  5. Kinships and genetic diversity (2) • Kinship coefficients from pedigrees • Between breed diversity • Within breed diversity relative to others • No/insufficient administration • => Use marker information

  6. Marker Estimated Kinships • Similarity score • Based on definition Malecot, 1948 • Correction for alleles Alike In State • not IBD

  7. Marker Estimated Kinships (2)Similarity Index Genotype x y Sxy AA AA 1 AA AB ½ AB AB ½ AB BC ¼ AB CD 0 • If Prob(AIS) = 0, E(Sxy) = fxy

  8. Marker Estimated Kinships (3)Correction for alleles Alike In State (AIS) • When Prob(AIS) > 0 • Sij,l = fij + (1 –fij)sl = sl + (1 –sl)fij • sl = Prob(AIS) for locus l • Estimate: • fij = (Sij,l – sl)/ (1 –sl)

  9. Marker Estimated Kinships (4)Definition of value of s • Assume a founder population P, in which all relations are zero • S(P) = s + (1 – s)fP = s • sl = sum(qil2), where qil allele frequency in P • If (A,B) oldest fission: s = mean(An, Bm) • Where n populations in cluster A and m populations in cluster B

  10. Marker Estimated Kinships (5)Linear estimation of s and f • ln(1 - S) = ln[(1-f)(1-s)] • = ln(1-f ) + ln(1-s ) • BLUP-like model: • ln(1-Sijl) = ln(1- fij) + ln(1-s0,l) • Yij,l = (Z + Xija) + Xlb

  11. Marker Estimated Kinships (6) • Mixed Model: •  = between and within population mean kinship • W = var[ln(1-Sijl)], gives priority to more informative loci • I = to regress fij back to mean

  12. Core set diversity (1) • c’Mc = mean(Kinship) • if c’Mc is small, genetic diversity is large • Adjust c so that average kinship is minimal

  13. Core set diversity (2)Definition of genetic diversity • The genetic diversity in a set of populations: • Div(M)= Div(cs) = 1 - fcs • Describes fraction of diversity of founder population left. • fP = 0 -> Div(P) = 1

  14. Application • 10 Dutch cattle populations • 11 Microsatellite markers Breeds Abrev. Marker loci # alleles Belgian Blue BBL BM1824 7 Dutch Red Pied DRP BM2113 12 Dutch Black Belted DBB ETH010 9 Limousine LIM ETH225 8 Holstein Friesian HF ETH003 11 Galloway GAL INRA23 11 Dutch Friesian DF SPS115 7 Improved Red Pied IRP TGLA122 23 Blonde d'Aquitaine BA TGLA126 8 Heck HCK TGLA227 14

  15. Application (2) • Kinship tree

  16. Application (3)

  17. Application (4)

  18. Conclusions (1)Genetic diversity and kinships • Defined: Gen Div  VG,W • VG,W = (1 - fW)VG • Gen Div proportional to (1 - fW) • Core set = Set with minimum mean kinship

  19. Conclusions (2)MEK and genetic diversity • Kinship matrix from MEK: • AIS • Definition of founder population • Measure between and within population diversity

  20. Conclusions (3)General • Not computer intensive • In theory no limits to Nbreeds • Extend to individuals • Results are promising

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